Proactive management of your corporate information has never been more important. Data governance isn’t a challenge solely for the IT team - it’s every inch a business issue. Seamless processes and a personal commitment to clean data give you the ability to generate accurate business intelligence and financial reports, and gain an instant snap shot of the health of your business. Most importantly, they also help you run a more intelligent, agile, fast-moving business than your competitors. The Data Governance Imperative is written from a business person’s view of data governance. This practical book covers both strategies and tactics around managing a data governance initiative.

• FOREWORD
• PREFACE
• ABOUT THE AUTHOR
• ACKNOWLEDGEMENTS
• CONTENTS
• CHAPTER 1: THE NEED FOR DATA GOVERNANCE
  • Sins of the past
  • Where are the metrics?
  • Unnecessary complexity
  • ETL and data warehouse
  • Building more efficiency
  • The value of an acquisition
  • Decisions based on gut
  • Data governance and being green
  • Improving compliance with BASEL II and SOX
  • Designated terrorist organizations
  • Why data governance?
  • Who needs data governance?
• CHAPTER 2: WHAT IS DATA GOVERNANCE?
  • How do different groups see data governance?
    • Broken data model example
  • Defining data governance by its benefits
    • Fewer adverse events
    • Building the IT–business bridge
• CHAPTER 3: DEFINING DATA GOVERNANCE SUCCESS
  • Generic data governance success factors
    • Fix data anomalies
    • Develop a repeatable process
    • Handle change
    • Coordinate efforts with business
    • Data ownership
  • Specific data governance success
• CHAPTER 4: GETTING FUNDED FOR IQ PROJECTS
  • The data champion
    • Knowledge
    • Develop cross-functional relationships
    • Selling the vision
    • Being positive
    • Leadership
  • Return on investment
  • Picking the right projects
  • Building credibility
  • Leveraging a crisis
  • Leveraging new initiatives
  • Conversation starters
    • The four whys
    • Return on investment
  • The “do nothing” option
  • Overcoming objections to data governance
    • Corporate revenue
    • Cheap wins
    • Case studies
    • Analysts
    • Data governance expert sessions
    • Guerilla marketing
• CHAPTER 5: PEOPLE – WHAT DOES A DATA GOVERNANCE TEAM LOOK LIKE?
  • Data governance roles and responsibilities
  • Data governance council
    • Third-party advisory
  • Team performance goals
  • Methodologies
• CHAPTER 6: PAINTING THE PICTURE
  • Mission statement
  • Communication strategies
  • Having productive meetings
  • Tools to communicate
    • Workflow
    • Wikis
    • Blogs
    • RSS feeds
  • Getting the data
  • Data governance workshops
  • Building a useful data quality scorecard
  • Views of data quality scorecard
  • Which key metrics do I track?
    • Level 1 – Raw data quality metrics
    • Level 2 – Business importance
    • Level 3 – Project importance
      • Example 1 – Customer relationship management system
      • Example 2 – Supply chain
    • Levels 4 – Business importance
    • Level 5 – Executive performance
    • Drill-downs
    • Scorecards, aggregations and compliance
• CHAPTER 7: FIXING YOUR DATA
  • Causes and actions
    • Cause 1: Receiving merger and acquisition data
      • Actions
    • Cause 2: Data ownership and control
      • Actions
    • Cause 3: Deliberately poor or apathetic data entry
      • Actions
    • Cause 4: Metadata standards
      • Actions
    • Cause 5: Incomplete or missing information
      • Actions
  • Information quality and data-intensive projects
  • Six phases of a data-intensive project
  • Phase 1:Project preparation
    • Define project team and roles
    • Identify business objectives
      • Scope
    • Analyze current technology
    • Assess data risks
  • Phase 2:Making the blueprint
    • Define success metrics
    • Communication strategy
    • Define standards
    • Access data
    • Analyze source data
    • Capture a baseline
    • Data architecture and schema/data model
    • Data architecture and platforms
    • Develop test case scenarios
    • Define exceptions process
  • Phase 3:Implement
    • Create user acceptance test plan
    • Create data-quality processes
    • QA initial results
    • Validate rules
    • Tune business rules and standards
  • Phase 4:Rollout preparation
    • Execute user acceptance test plan
    • User training and help desk training
    • Production system cutover plan
    • Successfully complete initial cleanse/load
  • Phase 5:Go live
    • Problem resolution
    • Post mortem
    • Define monitoring processes
  • Phase 6:Maintain
    • Announce successes
    • Collect new requirements for next phase
• CHAPTER 8: TECHNOLOGIES THAT SUPPORT DATA GOVERNANCE
  • Types of data governance technologies
  • Preventative
    • Type-ahead technology
    • Workforce management
    • Data quality dashboard
  • Diagnostic and health
    • Data profiling
    • Batch data quality
  • Infrastructure
    • Metadata
    • ETL
    • Master data management
    • Enterprise-class data quality
    • Data monitoring
  • Enrichment
    • Services and data sources
• CHAPTER 9: THE AUDACITY OF DATA GOVERNANCE
• CHAPTER 10: CASE STUDY – BT
  • Meager beginnings
  • Lessons learned
• ITG RESOURCES
  • Pocket Guides
  • Toolkits
  • Best Practice Reports
  • Training and Consultancy
  • Newsletter